| 1. |
- Antfolk, Christian, et al.
(författare)
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Artificial Redirection of Sensation From Prosthetic Fingers to the Phantom Hand Map on Transradial Amputees: Vibrotactile Versus Mechanotactile Sensory Feedback
- 2013
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 21:1, s. 112-120
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Tidskriftsartikel (refereegranskat)abstract
- This work assesses the ability of transradial amputees to discriminate multi-site tactile stimuli in sensory discrimination tasks. It compares different sensory feedback modalities using an artificial hand prosthesis in: 1) a modality matched paradigm where pressure recorded on the five fingertips of the hand was fed back as pressure stimulation on five target points on the residual limb; and 2) a modality mismatched paradigm where the pressures were transformed into mechanical vibrations and fed back. Eight transradial amputees took part in the study and were divided in two groups based on the integrity of their phantom map; group A had a complete phantom map on the residual limb whereas group B had an incomplete or nonexisting map. The ability in localizing stimuli was compared with that of 10 healthy subjects using the vibration feedback and 11 healthy subjects using the pressure feedback (in a previous study), on their forearms, in similar experiments. Results demonstrate that pressure stimulation surpassed vibrotactile stimulation in multi-site sensory feedback discrimination. Furthermore, we demonstrate that subjects with a detailed phantom map had the best discrimination performance and even surpassed healthy participants for both feedback paradigms whereas group B had the worst performance overall. Finally, we show that placement of feedback devices on a complete phantom map improves multi-site sensory feedback discrimination, independently of the feedback modality.
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| 2. |
- Boni, Irene, et al.
(författare)
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Restoring Natural Forearm Rotation in Transradial Osseointegrated Amputees
- 2018
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 26:12, s. 2333-2341
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Tidskriftsartikel (refereegranskat)abstract
- Osseointegrated transradial prostheses have the potential to preserve the natural range of wrist rotation, which improves the performance of activities of daily living and reduces compensatory movements that potentially lead to secondary health problems over time. This is possible by enabling the radius and the ulna bone to move with respect to each other, restoring the functionality of the original distal-radioulnar joint. In this paper, we report on psychophysics tests performed on an osseointegrated transradial amputee with the aim to understand the extent of mobility of the implants that is required to preserve the natural forearm rotation. Based on these experiments, we designed and developed an attachment device between the implants and the hand prosthesis that serves as an artificial distal radio-ulnar joint. This device was fitted on an osseointegrated transradial amputee and its functionality assessed by means of the Southampton Hand Assessment Procedure (SHAP) and the Minnesota Manual Dexterity test (MMDT). We found that the axial rotation of the implants is required to preserve forearm rotation, to distribute loads equally over the two implants (60% radius - 40% ulna), and to enable loading of the implants without unpleasant feelings for the patient. Higher function was recorded when our attachment device enabled forearm rotation: SHAP from 61 to 71, MMDT from 258s to 231s. Natural forearm rotation can be successfully restored in transradial amputees by using osseointegration and our novel mechanical attachment to the hand prosthesis.
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| 3. |
- Buist, Mirka, et al.
(författare)
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Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation
- 2024
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1558-0210 .- 1534-4320. ; 32, s. 1515-1523
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Tidskriftsartikel (refereegranskat)abstract
- Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.
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| 4. |
- Cipriani, Christian, et al.
(författare)
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Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees
- 2011
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 19:3, s. 260-270
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Tidskriftsartikel (refereegranskat)abstract
- A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.
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| 5. |
- Clemente, Francesco, et al.
(författare)
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Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses
- 2016
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 24:12, s. 1314-1322
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Tidskriftsartikel (refereegranskat)abstract
- Human grasping and manipulation control critically depends on tactile feedback. Without this feedback, the ability for fine control of a prosthesis is limited in upper limb amputees. Although various approaches have been investigated in the past, at present there is no commercially available device able to restore tactile feedback in upper limb amputees. Based on the Discrete Event-driven Sensory feedback Control (DESC) policy we present a device able to deliver short-lasting vibrotactile feedback to transradial amputees using commercially available myoelectric hands. The device (DESC-glove) comprises sensorized thimbles to be placed on the prosthesis digits, a battery-powered electronic board, and vibrating units embedded in an arm-cuff being transiently activated when the prosthesis makes and breaks contact with objects. The consequences of using the DESC-glove were evaluated in a longitudinal study. Five transradial amputees were equipped with the device for onemonth at home. Through a simple test proposed here for the first time-the virtual eggs test-we demonstrate the effectiveness of the device for prosthetic control in daily life conditions. In the future the device could be easily exploited as an add-on to complement myoelectric prostheses or even embedded in prosthetic sockets to enhance their control by upper limb amputees.
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| 6. |
- Crema, Andrea, et al.
(författare)
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A Wearable Multi-Site System for NMES-Based Hand Function Restoration
- 2018
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 26:2, s. 428-440
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Tidskriftsartikel (refereegranskat)abstract
- Reaching and grasping impairments significantly affect the quality of life for people who have experienced a stroke or spinal cord injury. The long-term well-being of patients varies greatly according to the restorable residual capabilities. Electrical stimulation could be a promising solution to restore motor functions in these conditions, but its use is not clinically widespread. Here, we introduce the HandNMES, an electrode array (EA) for neuromuscular electrical stimulation (NMES) aimed at grasp training and assistance. The device was designed to deliver electrical stimulation to extrinsic and intrinsic hand muscles. Six independent EAs, positioned on the user forearm and hand, deliver NMES pulses originating from an external stimulator equipped with demultiplexers for interfacing with a large number of electrodes. The garment was designed to be adaptable to user needs and anthropometric characteristics; size, shape, and contact materials can be customized, and stimulation characteristics such as intensity of stimulation and virtual electrode location, and size can be adjusted. We performed extensive tests with nine healthy subjects showing the efficacy of the HandNMES in terms of stimulation performance and personalization. Because encouraging results were achieved, in the coming months, the HandNMES device will be tested in pilot clinical trials.
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| 7. |
- Cubo, Rubén, et al.
(författare)
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Optimization-Based Contact Fault Alleviation in Deep Brain Stimulation Leads
- 2018
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1534-4320 .- 1558-0210. ; 26:1, s. 69-76
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Tidskriftsartikel (refereegranskat)abstract
- Deep brain stimulation (DBS) is a neurosurgical treatment in, e.g., Parkinsons Disease. Electrical stimulation in DBS is delivered to a certain target through electrodes implanted into the brain. Recent developments aiming at better stimulation target coverage and lesser side effects have led to an increase in the number of contacts in a DBS lead as well as higher hardware complexity. This paper proposes an optimization-based approach to alleviation of the fault impact on the resulting therapeutical effect in field steering DBS. Faulty contacts could be an issue given recent trends of increasing number of contacts in DBS leads. Hence, a fault detection/alleviation scheme, such as the one proposed in this paper, is necessary ensure resilience in the chronic stimulation. Two alternatives are considered and compared with the stimulation prior to the fault: one using higher amplitudes on the remaining contacts and another with alleviating contacts in the neighborhood of the faulty one. Satisfactory compensation for a faulty contact can be achieved in both ways. However, to designate alleviating contacts, a model-based optimization procedure is necessary. Results suggest that stimulating with more contacts yields configurations that are more robust to contact faults, though with reduced selectivity.
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| 8. |
- D'Accolti, Daniele, et al.
(författare)
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Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier
- 2023
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 31, s. 208-217
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Tidskriftsartikel (refereegranskat)abstract
- The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the \textit {transient} EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of 89%. Importantly, for each amputee, it produced at least one \textit {acceptable} combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to 80%), they were not as good with amputees (accuracy up to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.
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| 9. |
- D'Accolti, D., et al.
(författare)
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Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier
- 2023
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Ingår i: IEEE transactions on neural systems and rehabilitation engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 1534-4320 .- 1558-0210. ; 31, s. 208-217
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Tidskriftsartikel (refereegranskat)abstract
- The design of prosthetic controllers bymeans of neurophysiologicalsignals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of similar to 96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of similar to 89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist- hand movements (i.e., with accuracy > 85%). Regarding the cross-subject classifier, while our algorithm obtainedpromising resultswith non-amputees (accuracyup to similar to 80%), they were not as good with amputees (accuracy up to similar to 35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments.
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| 10. |
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